Selected work

Representative Papers

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

Sep 07, 2022

This work systematically investigates the underexplored problem of adversarial robustness in Spiking Neural Networks (SNNs). We find that white-box attacks against SNNs heavily rely on surrogate gradient techniques and exhibit severely limited transferability of adversarial examples across architectures (e.g., between SNNs and ViTs/CNNs). To address this, we first uncover a strong coupling mechanism between SNNs’ adversarial vulnerability and surrogate gradient estimation. Building on this insight, we propose Auto-SAGA—a cross-architecture universal white-box attack method that jointly optimizes adaptive self-attention gradient estimation and surrogate gradient approximation. Evaluated on CIFAR-10, CIFAR-100, and ImageNet, Auto-SAGA achieves a 91.1% improvement in attack success rate on SNN-ViT ensembles and attains three times the effectiveness of Auto-PGD on adversarially trained SNN ensembles, significantly outperforming existing baselines.

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FABO: Agent-Guided Discovery of Joint Breakpoint Optimization for Timing-Driven Routing Trees

Aug 31, 2026

研究通过语言模型引导的搜索方法，发现并优化了SALT算法在路径共享线分配上的局限性，提出了FABO算法以减少平均布线长度。

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A Process-Aware Hybrid Si/IGO Monolithic-3D 6T SRAM with BEOL Pass-Gates for the 2nm Node

Aug 23, 2026

研究提出了一种2nm节点的3D 6T SRAM，通过集成BEOL IGO通门与硅纳米片锁存器等技术，解决了芯片尺寸和性能优化问题。

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DiffPower: GPU-Accelerated Differentiable Switching Power Analysis and Optimization

Aug 04, 2026

This work addresses the longstanding trade-off between speed and accuracy in switching power analysis for physical design by introducing a differentiable power analysis framework based on a process-agnostic bytecode representation. By integrating reverse-mode automatic differentiation, GPU acceleration, and hybrid propagation—combining analytical modeling with parallel simulation—the proposed method enables, for the first time, gradient-based optimization for power consumption. This framework supports novel applications such as cell sizing optimization and power virus generation. Experimental results demonstrate up to a 1002× speedup in power analysis (with a switching activity correlation coefficient of 0.96) and a 904× acceleration in gradient computation compared to single-threaded CPU execution. Furthermore, cell sizing optimization reduces power by up to 2.98×, and power virus generation achieves a 2.13× improvement in efficiency.

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SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference

May 13, 2026

This work addresses the memory bottleneck in long-context inference caused by the linear growth of KV cache, which severely limits decoding efficiency. To overcome this challenge, the authors propose a joint optimization framework combining Angular-Domain Attention (ADA) and Rate-Distortion Retention (RDR). By parameterizing key vectors on a hypersphere, ADA computes attention scores directly in the angular domain, eliminating the need for dense reconstruction. RDR dynamically allocates precision and storage resources based on estimated future utility, enabling block-fusion-friendly computation. Under a fixed memory budget, the proposed method substantially reduces KV cache footprint and HBM traffic while preserving high decoding quality, achieving superior long-context inference performance in real-world deployment scenarios.

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Recent publications

Latest Papers

FABO: Agent-Guided Discovery of Joint Breakpoint Optimization for Timing-Driven Routing Trees

Aug 31, 2026

研究通过语言模型引导的搜索方法，发现并优化了SALT算法在路径共享线分配上的局限性，提出了FABO算法以减少平均布线长度。

0 citationsRead paper

A Process-Aware Hybrid Si/IGO Monolithic-3D 6T SRAM with BEOL Pass-Gates for the 2nm Node

Aug 23, 2026

研究提出了一种2nm节点的3D 6T SRAM，通过集成BEOL IGO通门与硅纳米片锁存器等技术，解决了芯片尺寸和性能优化问题。

0 citationsRead paper

DiffPower: GPU-Accelerated Differentiable Switching Power Analysis and Optimization

Aug 04, 2026

This work addresses the longstanding trade-off between speed and accuracy in switching power analysis for physical design by introducing a differentiable power analysis framework based on a process-agnostic bytecode representation. By integrating reverse-mode automatic differentiation, GPU acceleration, and hybrid propagation—combining analytical modeling with parallel simulation—the proposed method enables, for the first time, gradient-based optimization for power consumption. This framework supports novel applications such as cell sizing optimization and power virus generation. Experimental results demonstrate up to a 1002× speedup in power analysis (with a switching activity correlation coefficient of 0.96) and a 904× acceleration in gradient computation compared to single-threaded CPU execution. Furthermore, cell sizing optimization reduces power by up to 2.98×, and power virus generation achieves a 2.13× improvement in efficiency.

0 citationsRead paper

SPHERICAL KV: Angle-Domain Attention and Rate-Distortion Retention for Efficient Long-Context Inference

May 13, 2026

This work addresses the memory bottleneck in long-context inference caused by the linear growth of KV cache, which severely limits decoding efficiency. To overcome this challenge, the authors propose a joint optimization framework combining Angular-Domain Attention (ADA) and Rate-Distortion Retention (RDR). By parameterizing key vectors on a hypersphere, ADA computes attention scores directly in the angular domain, eliminating the need for dense reconstruction. RDR dynamically allocates precision and storage resources based on estimated future utility, enabling block-fusion-friendly computation. Under a fixed memory budget, the proposed method substantially reduces KV cache footprint and HBM traffic while preserving high decoding quality, achieving superior long-context inference performance in real-world deployment scenarios.

0 citationsRead paper

NSPOD: acceleratingthe convergence ofKrylov-based iterative linearsolvers via approximated PODs

May 08, 2026

This work addresses the high sensitivity of Krylov iterative solvers to geometry, boundary conditions, and material parameters when solving parametric partial differential equations, as well as the limited generalization and acceleration capabilities of existing neural operator-based preconditioners. The authors propose NSPOD, a multigrid-like deep operator network preconditioner that, for the first time, integrates neural operators with Proper Orthogonal Decomposition (POD) subspaces. By approximating solutions within a low-dimensional POD subspace, NSPOD effectively accelerates Krylov solvers without requiring retraining, even on unstructured meshes derived from complex CAD geometries. Demonstrated on linear PDEs in solid mechanics, the method significantly reduces iteration counts and outperforms state-of-the-art preconditioners such as algebraic multigrid, thereby overcoming key performance bottlenecks in current approaches.

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